In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs).
As an alternative, we investigate a simple strategy of random force injection (RFI) to perturb system dynamics during training.
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.
To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality.
We demonstrate our method on a cart-pole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality.
Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization.
We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects in the scene.
Motion Planning Robotics Systems and Control Systems and Control
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
2 code implementations • 11 Sep 2019 • Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, Guilhem Saurel, Bilal Hammoud, Maximilien Naveau, Justin Carpentier, Ludovic Righetti, Sethu Vijayakumar, Nicolas Mansard
Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP).
Robotics Optimization and Control